High-throughput biology, exemplified by DNA microarrays for gene expression and SNP genotyping, array comparative genomic hybridization, forward- and reverse-phase proteomic assays, and assays for epigenetic processes such as methylation, is rapidly generating enormous observation sets. Biologists seeking to make sense of this growing body of data need to have a firm grasp of statistical methodology. The primary objective of the Cold Spring Harbor summer course in Integrated Data Analysis For High Throughput Biology (to be held 2007-2011) is to build competence in quantitative methods for the analysis of high-throughput molecular biology data, from which meaningful inferences about biological processes can be drawn. Such complete and in-depth training in this emerging technology is currently unavailable elsewhere. The program seeks to provide the opportunity for course participants to learn the fundamental principles and the most recent concepts in applying quantitative methods to the analysis of high-throughput molecular biology data, with emphasis on interpretation and integration of microarray datasets, and the joint interpretation of gene expression patterns and features of genomic sequence;provide the opportunity for research workers in diverse fields of biology to become familiar with the techniques and principles of data analysis and statistical inference along with methods of statistical computing to implement these techniques;provide talented graduate students and postdoctoral fellows in this area with an opportunity to work, study and associate with outstanding research workers, including biologists who experimentally generate high-throughput databases and statistical methodologists who create new interpretive and computational frameworks. The aim of this course is to provide intensive hands-on training over a two week period that will prepare the participant to initiate the analysis of large and complex biological data sets, as exemplified by the data arising from DNA microarray experiments. Work can then be directly applied to complex studies of mutation, gene expression and other "whole-genome" studies in a wide range of organisms and tissues, not least in the study of cancer where the hallmark of aberrant genomes particularly requires the use of these whole-genome approaches together with advanced statistical methodologies to interpret the results.